Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts. Many machine learning-based approaches have been developed for the prognosis and diagnosis of COVID-19 from medical images and this Analysis identifies over 2,200 relevant published papers and preprints in this area. After initial screening, 62 studies are analysed and the authors find they all have methodological flaws standing in the way of clinical utility. The authors have several recommendations to address these issues.

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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Background: COVID-19 is a global pandemic. The prevention of SARS-CoV-2 infection and the rehabilitation of survivors are currently the most urgent tasks. However, after patients with COVID-19 are discharged from the hospital, how long the antibodies persist, whether the lung lesions can be completely absorbed, and whether cardiopulmonary abnormalities exist remain unclear. Methods: A total of 56 COVID-19 survivors were followed up for 12 months, with examinations including serum virus-specific antibodies, chest CT, and cardiopulmonary exercise testing. Results: The IgG titer of the COVID-19 survivors decreased gradually, especially in the first 6 months after discharge. At 6 and 12 months after discharge, the IgG titer decreased by 68.9 and 86.0%, respectively. The IgG titer in patients with severe disease was higher than that in patients with non-severe disease at each time point, but the difference did not reach statistical significance. Among the patients, 11.8% were IgG negative up to 12 months after discharge. Chest CT scans showed that at 3 and 10 months after discharge, the lung opacity had decreased by 91.9 and 95.5%, respectively, as compared with that at admission. 10 months after discharge, 12.5% of the patients had an opacity percentage >1%, and 18.8% of patients had pulmonary fibrosis (38.5% in the severe group and 5.3% in the non-severe group, P 30%. Conclusions: IgG antibodies in most patients with COVID-19 can last for at least 12 months after discharge. The IgG titers decreased significantly in the first 6 months and remained stable in the following 6 months. The lung lesions of most patients with COVID-19 can be absorbed without sequelae, and a few patients in severe condition are more likely to develop pulmonary fibrosis. Approximately one-fifth of the patients had cardiopulmonary dysfunction 6 months after discharge.

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Antibodies Can Last for More Than 1 Year After SARS-CoV-2 Infection: A Follow-Up Study From Survivors of COVID-19

Phenotypes and Subphenotypes of Patients With COVID-19: A Latent Class Modeling Analysis.

Chest CT is prognostic in Covid-19 but there is a lack of consensus on how to report the CT findings. A chest CT scoring system, OCoS, was implemented in clinical routine on 1 April 2020, in Orebro...

Visual scoring of chest CT at hospital admission predicts hospitalization time and intensive care admission in Covid-19.

Imaging plays an important role in the detection of coronavirus (COVID-19) pneumonia in both managing the disease and evaluating the complications. Imaging with chest computed tomography (CT) can also have a potential predictive and prognostic role in COVID-19 patient outcomes. The aim of this pictorial review is to describe the role of imaging with chest X-ray (CXR), lung ultrasound (LUS), and CT in the diagnosis and management of COVID-19 pneumonia, the current indications, the scores proposed for each modality, the advantages/limitations of each modality and their role in detecting complications, and the histopathological correlations.

https://www.mdpi.com/2075-4418/11/3/437/pdf

A Pictorial Review of the Role of Imaging in the Detection, Management, Histopathological Correlations, and Complications of COVID-19 Pneumonia.

Background. Chest CT findings have the potential to guide management in hospitalized patients with COVID-19. Objective. To explore a CT visual severity score in hospitalized patients with COVID-19, with attention to temporal changes in the score and the role of the score in a model for predicting in-hospital complications. Methods. This retrospective study included 161 inpatients with COVID-19 from three hospitals in China who underwent serial chest CT scans during hospitalization. CT examinations were evaluated using a visual severity scoring system. The temporal pattern of the CT visual severity score across serial CT examinations during hospitalization was characterized using a generalized spline regression model. A prognostic model to predict major complications, including in-hospital mortality, was created using the CT visual severity score and clinical variables. External model validation was performed in a cohort of 135 patients from a different hospital, evaluated by two independent radiologists. Results. The cohort included 91 survivors with non-severe disease, 55 survivors with severe disease, and 15 patients who died during hospitalization. Median CT visual lung severity score in the first week of hospitalization was 2.0 in survivors with non-severe disease, 4.0 in survivors with severe disease, and 11.0 in non-survivors. CT visual severity score peaked on hospitalization day 9 and 12 in survivors with non-severe and severe disease, respectively, and progressively decreased in subsequent hospitalization weeks in both groups. In the prognostic model, in-hospital complications were independently associated with a severe CT score (odds ratio [OR] 31.28), moderate CT score (OR 5.86), age (OR 1.09 per year increase), and lymphocyte count (OR 0.03 per 1*10^9/L increase). In the validation cohort, the two readers achieved C-index of 0.92-0.95, accuracy 85.2%-86.7%, sensitivity 70.7%-75.6%, and specificity 91.4%-91.5% for predicting in-hospital complications. Conclusion. A CT visual severity score is associated with clinical disease severity and evolves in a characteristic fashion during hospitalization for COVID-19. A prognostic model based on the CT visual severity score and clinical variables has strong performance in predicting in-hospital complications. Clinical Impact. The prognostic model using the CT visual severity score may help identify patients at highest risk of poor outcomes and guide early intervention.

https://www.ajronline.org/doi/pdf/10.2214/AJR.20.24044

Multicenter Study of Temporal Changes and Prognostic Value of a CT Visual Severity Score in Hospitalized Patients With Coronavirus Disease (COVID-19).

Tissue remodeling/fibrosis is a main feature of idiopathic pulmonary fibrosis (IPF), which results in the replacement of normal lung parenchyma with a collagen-rich extracellular matrix produced by fibroblasts and myofibroblasts. Epithelial-mesenchymal transition (EMT) in type 2 lung epithelial cells is a key process in IPF, which leads to fibroblasts and myofibroblasts accumulation and excessive collagen deposition. DEC1, a structurally distinct class of basic helix-loop-helix proteins, is associated with EMT in cancer. However, the functional role of DEC1 in pulmonary fibrosis (PF) remains elusive. Herein, we aimed to explore DEC1 expression in IPF and bleomycin (BLM)-induced PF in mice and the mechanisms underlying the fibrogenic effect of DEC1 in PF in vivo and in vitro by Dec1-knockout (Dec1 −/−) mice, knockdown and overexpression of DEC1 in alveolar epithelial cells (A549 cells). We found that the expression of DEC1 was increased in IPF and BLM-injured mice. More importantly, Dec1 −/− mice had reduced PF after BLM challenge. Additionally, DEC1 deficiency relieved EMT development and repressed the PI3K/AKT/GSK-3β/β-catenin integrated signaling pathway in mice and in A549 cells, whereas DEC1 overexpression in vitro had converse effects. Moreover, the PI3K/AKT and Wnt/β-catenin signaling inhibitors, LY294002 and XAV-939, ameliorated BLM-meditated PF in vivo and relieved EMT in vivo and in vitro. These pathways are interconnected by the GSK-3β phosphorylation status. Our findings indicated that during PF progression, DEC1 played a key role in EMT via the PI3K/AKT/GSK-3β/β-catenin integrated signaling pathway. Consequently, targeting DEC1 may be a potential novel therapeutic approach for IPF.

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Dec1 Deficiency Ameliorates Pulmonary Fibrosis Through the PI3K/AKT/GSK-3β/β-Catenin Integrated Signaling Pathway

Plasma IL-36α and IL-36γ as Potential Biomarkers in Interstitial Lung Disease Associated with Rheumatoid Arthritis: a Pilot Study in the Chinese Population

Xingxing Hu

Papers

Multicenter Study of Temporal Changes and Prognostic Value of a CT Visual Severity Score in Hospitalized Patients With Coronavirus Disease (COVID-19).

Dec1 Deficiency Ameliorates Pulmonary Fibrosis Through the PI3K/AKT/GSK-3β/β-Catenin Integrated Signaling Pathway

Plasma IL-36α and IL-36γ as Potential Biomarkers in Interstitial Lung Disease Associated with Rheumatoid Arthritis: a Pilot Study in the Chinese Population